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Non-Covalent Interactions on Polymer-Graphene Nanocomposites and Their Effects on the Electrical Conductivity

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Non-Covalent Interactions on Polymer-Graphene Nanocomposites and Their Effects on the Electrical Conductivity polymers Article Non-Covalent Interactions on Polymer-Graphene Nanocomposites and Their Effects on the Electrical Conductivity Jorge Luis Apátiga 1, Roxana Mitzayé del Castillo 1 , Luis Felipe del Castillo 2, Alipio G. Calles 1, Raúl Espejel-Morales 1, José F. Favela 3 and Vicente Compañ 4,* 1 Departamento de Física, Facultad de Ciencias, Universidad Nacional Autónoma de México, Circuito Interior s/n, Ciudad Universitaria, Mexico City 04510, Mexico; [email protected] (J.L.A.); [email protected] or [email protected] (R.M.d.C.); [email protected] (A.G.C.); [email protected] (R.E.-M.) 2 Departamento de Polímeros, Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito Interior s/n, Ciudad Universitaria, Mexico City 04510, Mexico; [email protected] 3 Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Circuito Interior s/n, Ciudad Universitaria, Mexico City 04510, Mexico; [email protected] 4 Departamento de Termodinámica Aplicada, Escuela Técnica Superior de Ingenieros Industriales (ETSII), Campus de Vera s/n, Universitat Politécnica de Valencia, 46020 Valencia, Spain * Correspondence: [email protected]; Tel.: +34-963-879-328 Abstract: It is well known that a small number of graphene nanoparticles embedded in polymers enhance the electrical conductivity; the polymer changes from being an insulator to a conductor. The Citation: Apátiga, J.L.; del Castillo, graphene nanoparticles induce several quantum effects, non-covalent interactions, so the percola- R.M.; del Castillo, L.F.; Calles, A.G.; tion threshold is accelerated. We studied five of the most widely used polymers embedded with Espejel-Morales, R.; Favela, J.F.; graphene nanoparticles: polystyrene, polyethylene-terephthalate, polyether-ketone, polypropylene, Compañ, V. Non-Covalent and polyurethane. The polymers with aromatic rings are affected mainly by the graphene nanoparti- Interactions on Polymer-Graphene cles due to the π-π stacking, and the long-range terms of the dispersion corrections are predominant. Nanocomposites and Their Effects on The polymers with linear structure have a CH-π stacking, and the short-range terms of the dispersion the Electrical Conductivity. Polymers 2021, 13, 1714. https://doi.org/ corrections are the important ones. We used the action radius as a measuring tool to quantify the 10.3390/polym13111714 non-covalent interactions. This action radius was the main parameter used in the Monte-Carlo simulation to obtain the conductivity at room temperature (300 K). The action radius was the key tool Academic Editors: Artur Pinto and to describe how the percolation transition works from the fundamental quantum levels and connect Fernão D. Magalhães the microscopic study with macroscopic properties. In the Monte-Carlo simulation, it was observed that the non-covalent interactions affect the electronic transmission, inducing a higher mean-free Received: 27 April 2021 path that promotes the efficiency in the transmission. Accepted: 21 May 2021 Published: 24 May 2021 Keywords: polymer-graphene nanocomposites; conductivity; Monte-Carlo; quantum effects; non- covalent interactions; dispersion correction; polystyrene; polyethylene-terephthalate; polyether- Publisher’s Note: MDPI stays neutral ketone; polypropylene; polyurethane with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction The science of polymeric materials has revolutionized today’s science and technology. Today, almost every industrial component has a polymer in it [1]; there is a great variety of Copyright: © 2021 by the authors. experimental studies on this topic [2–9]. From this experimental research, it is known that Licensee MDPI, Basel, Switzerland. embedding a small number of nanoparticles can change the physical properties of the poly- This article is an open access article mer, e.g., from insulator to conductor [10–12]. The most popular fillers that exist are carbon distributed under the terms and conditions of the Creative Commons nanotubes [13–15], graphene [16], and several oxides [17]. These nanocomposites have a Attribution (CC BY) license (https:// vast spectrum of applications, such as the development of nanoelectronics [18], aerospace creativecommons.org/licenses/by/ industry [19], membrane separation [20], anti-corrosives [21,22], super-capacitors [23,24], 4.0/). or sensors of several interesting molecules as organic molecules [25–27]. In particular, Polymers 2021, 13, 1714. https://doi.org/10.3390/polym13111714 https://www.mdpi.com/journal/polymers Polymers 2021, 13, 1714 2 of 14 carbon nanotubes and graphene nanofillers have proven to drastically increase electrical conductivity. With only a small percentage of particles introduced into the polymer, it is possible to achieve a high conductivity, or even ballistic conductivity, characteristic of pristine graphene [28–30]. Experimental and computational studies of conductivity in nanocomposite polymer- graphene present a change in the electrical behavior according to a sigmoid curve [31–33]. It is known that the electrical conductivity in some compounds is a sigmoid curve and that the pertinent theory to describe this type of system is to use the percolation theory. The percolation theory is a well-known tool of statistical physics and stochastic processes which has been used often to predict the conductivity of several materials [34,35]. Incorporating the percolation tools and stochastic analysis has led to successful results in terms of modeling electrical conductivity [36,37]. Besides the knowledge of this fact, some details are unknown for polymer-graphene nanocomposites. One of the most interesting facts about the use of carbon nanotubes and graphene nanoparticles as fillers is that the critical point or percolation threshold (PT) occurs with a minimal quantity of nanofillers. The percolation threshold for carbon nanotubes embedded in polypropylene is around 5% [38,39], whereas, for graphene fillers, the percolation threshold is approximately 18% [31]. It is well known that, to describe the electrical conductivity correctly, it is necessary to consider a proper aspect ratio [31,40]. Our group has proved that quantum effects are the main reason for the percolation threshold occurring so rapidly, and we are developing a methodology to calculate the con- ductivity of these nano-compounds theoretically. In this work, we simulated the nanocom- posites at the microscopic level using an ab-initio computational framework to characterize the quantum effects correctly. Then, we approached the macroscopical level with a Monte- Carlo simulation, which has been proven to give accurate results [41–44]. Specifically, on the Monte-Carlo simulation, we used the phase-type distribution theory to incorporate the quantum effects in the potential and the conductance estimation. The aspect parameter or action radius was obtained directly from the ab-initio simulation and then incorporated into the Monte-Carlo simulation. To test the methodology and the working hypothesis, we simulated different polymer-graphene nanocomposite membranes: polystyrene graphene (PS-graphene), polyethylene-terephthalate graphene (PET-graphene), polyether-ketone graphene (PEK-graphene), poly-propylene graphene (PP-graphene), and polyurethane graphene (PU-graphene). We compared our outcomes with several experimental results from different authors to validate our model. 2. Method 2.1. Ab-Initio Calculations The ab-initio calculations were made to characterize the systems’ quantum effects appropriately and then to incorporate them in the macroscopic simulation and phase conductivity analysis. The DFT calculations were carried out using the Perdew–Burke–Ernzerhof (PBE) functional [45], Grimme dispersion corrections Bj [46], and 6-311G * (d,p) basis set. We used the computational package Gaussian 16 [47]. The initial structures were placed on a graphene cluster at an initial distance of 2.5 Å. The graphene cluster was designed as a 3 × 4 graphene layer with hydrogen at the edges. The polymer molecules were placed over the graphene sheet in different positions, making a systematic search of the ground-state configurations, see Figure1. After the adsorption sites and adsorption energies of the ground-state configuration were calculated, we evaluated the deformation to obtain the aspect ratio of the percolation simulation. The adsorption energy was computed with the following equation: h i Eads = Epol+graphene − Epol + Egraphene (1) More details of the ab-initio calculations are presented in the Supporting Informa- tion S1. Polymers 2021, 13, 1714 3 of 14 Polymers 2021, 13, x FOR PEER REVIEW 3 of 15 Figure 1. The polymer is placedFigure in several 1. The positions polymer overis placed the graphene in several cluster, positions and ov thener allthe the graphene systems cluster, are optimized and then to all find the the ground-state structure. Thesystems systematic are optimized search of to the find stabilized the ground-state structure startedstructure. with The these systematic configurations: search of horizontal, the stabilized vertical, and vertical1 for eachstructure polymer started with graphene. with these configurations: horizontal, vertical, and vertical1 for each polymer with graphene. 2.2. Stochastic Simulation TheMore stochastic details of simulation the ab-initio is made calculations to emulate are presented the experimental in the Supporting situation as Information accurately asS1. possible. The simulation
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